Capabilities Overview
This section answers: What can CubeDynamics do once your data is in a cube? Use it to scan the verb surface and decide which operations to combine.
In this section you'll find:
- Supported inputs for pipe(...) and how they map into cubes or geometries.
- Highlights of structural, statistical, event, and visualization verbs.
- Example pipelines that show how verbs compose.
Key links: - Verbs & Examples - Pipe and verbs - Cube viewer (v.plot) - Event extraction and vases
Data sources you can pipe
pipe(...) accepts inputs ranging from xarray objects to helper loaders and VirtualCubes. Use What is a cube? and the dataset pages to choose the right starting point.
Core verbs at a glance
Verbs progress from inspection to aggregation, then to statistics, events, and visualization. Examples include:
- Structural helpers such as apply, flatten_space, and to_netcdf.
- Statistical verbs like mean, variance, anomaly, and zscore.
- Event-aware verbs including extract, vase, and fire-focused plots.
- Visualization verbs such as plot, map, and climate_hist that return the original object so pipelines can continue.
Putting it together
Combine data loaders, verbs, and viewers to build readable pipelines:
from cubedynamics import pipe, verbs as v
(pipe(cube)
| v.anomaly()
| v.variance(dim="time", keep_dim=True)
| v.plot(title="Spatiotemporal Variance")
)
The same grammar works whether you stream data or work in memory.